Publications by authors named "S Wojcinski"
Article Synopsis
- Some breast cancer patients don’t fully respond to treatment, which makes it harder for them to recover.
- Researchers looked at a special biopsy method called VAB to see if it could help detect these patients before surgery.
- They found that VAB always showed if there was leftover cancer after treatment, while regular imaging methods weren't as reliable.
Alongside mammography, breast ultrasound is an important and well-established method in assessment of breast lesions. With the "Best Practice Guideline", the DEGUM Breast Ultrasound (in German, "Mammasonografie") working group, intends to describe the additional and optional application modalities for the diagnostic confirmation of breast findings and to express DEGUM recommendations in this Part II, in addition to the current dignity criteria and assessment categories published in Part I, in order to facilitate the differential diagnosis of ambiguous lesions.The present "Best Practice Guideline" has set itself the goal of meeting the requirements for quality assurance and ensuring quality-controlled performance of breast ultrasound.
View Article and Find Full Text PDFObjectives: We evaluated whether lesion-to-fat ratio measured by shear wave elastography in patients with Breast Imaging Reporting and Data System (BI-RADS) 3 or 4 lesions has the potential to further refine the assessment of B-mode ultrasound alone in breast cancer diagnostics.
Methods: This was a secondary analysis of an international diagnostic multicenter trial (NCT02638935). Data from 1288 women with breast lesions categorized as BI-RADS 3 and 4a-c by conventional B-mode ultrasound were analyzed, whereby the focus was placed on differentiating lesions categorized as BI-RADS 3 and BI-RADS 4a.
Background: Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.
Methods: We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses.
Article Synopsis
- AI algorithms for medical image analysis showed they can perform as well as human readers in breast cancer diagnosis but need to incorporate multiple data sources for better accuracy.
- * In a study with 1288 women, both human experts and AI using ultrasound data alone had similar success rates in diagnosing breast masses.
- * However, when integrating additional clinical and demographic information, AI algorithms performed better, yet both still lagged behind traditional routine diagnosis methods.